IT AI Index

Index › Vendors › Voldemort · September 2026 Edition

1 category · Named, not ranked

Voldemort

3Judge labels

0First choices

3Negative labels

2 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

1 label, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Voldemort was named 1 time in NoSQL, where MongoDB led with 59%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small businessnot named

No model named it for this buyer.

Mid-market66 of 72

under the label threshold

Enterprise44 of 48

under the label threshold

In nosql · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Voldemort for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
NoSQL databases	Data platform	0%	66 of 72	100%	1	under 10 labels · led by MongoDB at 59%

Movement

This is the first edition on this tier, so no move can be computed for Voldemort yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated Voldemort across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Negative	Labels
Claude Haiku 4.5	0	0
GPT-5.4 mini	0	0
Gemini 3.5 Flash	0	0
Perplexity Sonar	0	0
Grok 4.1 Fast	1	1
Mistral Small	0	0
DeepSeek V4 Flash	0	0
Llama 4 Maverick	0	0
Qwen 3.7 Flash	0	0
Kimi K2	0	0
GLM 4.7 FlashX	0	0
MiniMax M2.5	0	0

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct0 labelsNone

Paraphrase0 labelsNone

Comparative0 labelsNone

Budget-constrained0 labelsNone

Scale-constrained0 labelsNone

Negative3 labelsNone

First choiceAlternativeMentionNegative3 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

No positive label carried a quote.

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“Abandoned/deprecated projects ... Voldemort” Grok 4.1 Fast · NoSQL · negative prompt · hard negative

Named alongside

The products named in the same answers as Voldemort, over the 3 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Voldemort was named but was not.

ProductSame answerTook the first choice insteadHead to head

Apache CouchDB3 of 30Not in the top three

Neo4j3 of 30Not in the top three

Redis3 of 30Not in the top three

Apache Cassandra2 of 31Not in the top three

Amazon DynamoDB2 of 30Not in the top three

Couchbase2 of 30Not in the top three

MongoDB2 of 30Not in the top three

Riak2 of 30Not in the top three

ScyllaDB2 of 30Not in the top three

DataStax1 of 31Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named Voldemort. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 3 of the 3 answers that named Voldemort and are not a share of its labels.

Domains cited

aerospike.com3

voltactivedata.com3

era.library.ualberta.ca2

infoq.com2

journalofbigdata.springeropen.com2

mongodb.com2

scylladb.com2

softwareengineering.stackexchange.com2

acloudfan.com1

alex-klaus.com1

Twenty of the twenty domain citations in answers naming Voldemort came from somebody else's page.

Pages cited

aerospike.com/blog/ten-best-nosql-cloud-databases3

voltactivedata.com/blog/2021/10/nosql-due-diligence-part-13

infoq.com/articles/nosql-in-the-enterprise2

journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0025-02

mongodb.com/resources/compare/cassandra-vs-mongodb2

acloudfan.com/2023/03/11/nosql-databases-are-for-large-companies-only1

alex-klaus.com/reasons-not-to-nosql1

byteplus.com/en/topic/4030491

Pages are listed as the models cited them.

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Voldemort's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Voldemort, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at voldemort.com is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.